system

Abstract

The system according to the embodiment aims to provide appropriate and prompt answers to problems and questions that users face. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives questions from users. The analysis unit analyzes the questions received by the reception unit. The generation unit generates answers based on the questions analyzed by the analysis unit. The provision unit provides the answers generated by the generation unit.

Description

[Technical Field]

[0001] The technology of the present disclosure relates to a system. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have had the problem of making it difficult for users to obtain appropriate and prompt answers to the various problems and questions they face.

[0005] The system according to the embodiment aims to provide appropriate and prompt answers to problems and questions that users face. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives a question from a user. The analysis unit analyzes the question received by the reception unit. The generation unit generates an answer based on the question analyzed by the analysis unit. The provision unit provides the answer generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide appropriate and prompt answers to problems and questions that users face. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

[0011] In the following embodiments, a coded processor (hereinafter simply referred to as a "processor") may be a single arithmetic device or a combination of multiple arithmetic devices. Furthermore, the processor may be a single type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

[0013] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

[0016] [First embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] 1, a data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

[0023] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] 2, in the data processing device 12, a specific process is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A system according to an embodiment of the present invention utilizes the wisdom of historical figures to solve various problems and questions faced by users. In this system, users input questions via video chat, and a generative AI analyzes the questions and generates answers based on the wisdom of historical figures. The system then delivers the answers to users in real time. For example, if a user inputs a question such as, "I'm thinking about a new business strategy. How should I proceed?", the generative AI generates answers based on the strategic thinking of Napoleon Bonaparte and the innovative ideas of Steve Jobs and delivers them in real time. This system allows users to receive fast and accurate advice. For example, if a student asks, "What is the most efficient way to study for exams?", the system provides advice based on Einstein's study methods and Benjamin Franklin's time management techniques. This system helps users solve problems and offers new perspectives and concrete solutions. For example, this system can provide fast and accurate advice not only to business leaders, but also to students, teachers, and anyone faced with daily decision-making.

[0029] A problem solving support system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives questions from users. For example, users can input questions through video chat. The analysis unit uses a generation AI to analyze the questions received by the reception unit. For example, the generation AI understands and analyzes the content of the questions using natural language processing technology. The generation unit uses the generation AI to generate answers based on the questions analyzed by the analysis unit. For example, the generation AI generates answers by referring to past data and case studies. The provision unit provides the answers generated by the generation unit to users in real time. For example, the answers are provided through video chat. This enables the problem solving support system according to an embodiment to efficiently analyze users' questions and provide appropriate answers.

[0030] The analysis unit can analyze the question using natural language processing technology. Natural language processing technology includes, for example, morphological analysis, grammatical analysis, and semantic analysis. The analysis unit, for example, uses morphological analysis to divide the words in the question and analyze the meaning of each word. The analysis unit can also use grammatical analysis to analyze the sentence structure of the question and understand the meaning of the sentence. Furthermore, the analysis unit can use semantic analysis to understand the intent of the question and extract information for generating an appropriate answer. As a result, the use of natural language processing technology improves the accuracy of question analysis.

[0031] The generation unit can generate an answer by referring to past data or examples. Past data and examples include, for example, a past FAQ database and a user's question history. The generation unit can, for example, refer to a past FAQ database to generate an answer to a similar question. The generation unit can also refer to the user's question history to generate a new answer based on answers provided to similar questions in the past. Furthermore, the generation unit can also refer to past examples to generate an answer based on a specific example. In this way, by referring to past data and examples, the reliability of the answer is improved.

[0032] The providing unit can provide answers in real time through video chat. Specific video chat technologies include, for example, a video chat platform to be used, a communication protocol, and the like. The providing unit provides answers to users using, for example, a specific video chat platform. The providing unit can also use a specific communication protocol to achieve real-time communication. Furthermore, the providing unit can adjust the image quality and sound quality of the video chat to provide high-quality answers to users. This improves user convenience by providing answers in real time through video chat.

[0033] The problem-solving support system includes a UI unit that manages the user interface. The UI unit manages the user interface. For example, the UI unit designs the screen layout to allow users to operate it intuitively. The UI unit can also define operation methods to allow users to easily input questions. Furthermore, the UI unit can manage the displayed content and provide the user with the information they need appropriately. In this way, by managing the user interface, user operability is improved.

[0034] The problem solving support system includes a chat unit that provides a video chat function. The chat unit provides the video chat function. For example, the chat unit manages the image quality and sound quality of the video chat to provide the user with a high-quality video chat experience. The chat unit can also use a specific communication protocol to achieve real-time communication. Furthermore, the chat unit can monitor the connection status of the video chat to prevent the connection from being interrupted. As a result, the video chat function is provided, allowing the user to ask questions in real time.

[0035] The reception unit can analyze the user's past question history and select the optimal reception method. For example, the reception unit can preferentially receive question formats that the user has frequently used in the past. The reception unit can also preferentially receive questions related to topics that the user has previously liked. Furthermore, the reception unit can also suggest the optimal reception time based on the user's past question history. In this way, the optimal reception method can be selected by analyzing the user's past question history.

[0036] When receiving a question, the reception unit can filter the questions based on the user's current areas of interest. For example, the reception unit can only accept questions related to topics in which the user is currently interested. The reception unit can also filter related questions based on the user's recent search history. Furthermore, the reception unit can analyze the user's social media activity and filter questions based on the user's areas of interest. In this way, by filtering questions based on the user's areas of interest, highly relevant questions can be accepted.

[0037] When accepting a question, the acceptance unit can select an appropriate acceptance means depending on the user's input method. For example, if the user prefers voice input, the acceptance unit can preferentially accept voice input. Also, if the user prefers text input, the acceptance unit can preferentially accept text input. Furthermore, if the user uses an image to ask a question, the acceptance unit can perform image analysis and accept the question. This improves user convenience by selecting the optimal acceptance means depending on the user's input method.

[0038] When accepting questions, the acceptance unit can prioritize accepting relevant questions by taking into account the user's geographical location information. For example, if the user is in a specific area, the acceptance unit can prioritize accepting questions related to that area. Furthermore, if the user is traveling, the acceptance unit can also prioritize accepting questions related to the user's travel destination. Furthermore, if the user is at home, the acceptance unit can also prioritize accepting questions related to daily life. In this way, by taking into account the user's geographical location information, highly relevant questions can be prioritized.

[0039] When receiving a question, the reception unit can analyze the user's social media activity and receive related questions. For example, the reception unit receives questions related to topics in which the user has shown interest on social media. The reception unit can also receive related questions based on the content of the user's social media posts. Furthermore, the reception unit can also receive related questions by referring to the activities of the user's friends on social media. In this way, highly relevant questions can be received by analyzing the user's social media activity.

[0040] The reception unit can adjust the reception method by reflecting the user's past feedback when receiving a question. For example, the reception unit preferentially uses the reception method that the user has previously preferred. The reception unit can also suggest the optimal reception method based on the user's past feedback. Furthermore, the reception unit can also customize the reception interface by reflecting the user's past feedback. In this way, the optimal reception method can be provided by reflecting the user's past feedback.

[0041] When analyzing a question, the analysis unit can adjust the accuracy of the analysis based on the importance of the question. For example, the analysis unit performs a detailed analysis on a question with a high level of importance. The analysis unit can also perform a simplified analysis on a question with a low level of importance. Furthermore, the analysis unit can allocate analysis resources according to the importance of the question. This allows for efficient allocation of resources by adjusting the level of detail of the analysis based on the importance of the question.

[0042] When analyzing a question, the analysis unit can apply different analysis algorithms depending on the category of the question. For example, the analysis unit applies a business-oriented analysis algorithm to a business-related question. The analysis unit can also apply a learning-oriented analysis algorithm to a learning-related question. Furthermore, the analysis unit can also apply an everyday life-oriented analysis algorithm to a question about everyday life. In this way, by applying an analysis algorithm depending on the question category, the accuracy of the analysis is improved.

[0043] When analyzing a question, the analysis unit can improve the accuracy of the analysis by referring to the user's past question results. The analysis unit improves the accuracy of the analysis, for example, based on the user's past question results. The analysis unit can also analyze the user's past question results and optimize the analysis algorithm. Furthermore, the analysis unit can adjust the analysis parameters by referring to the user's past question results. In this way, the accuracy of the analysis is improved by referring to the user's past question results.

[0044] When analyzing questions, the analysis unit can determine the order of analysis based on the time of submission of the questions. The analysis unit can determine the priority of analysis based on, for example, the time period in which the questions were submitted. The analysis unit can also determine the priority of analysis based on the date in which the questions were submitted. Furthermore, the analysis unit can also determine the priority of analysis based on the timing in which the questions were submitted. In this way, by determining the priority of analysis based on the time in which the questions were submitted, timely analysis is possible.

[0045] When analyzing questions, the analysis unit can adjust the order of analysis based on the relevance of the questions. For example, if a question has high relevance, the analysis unit prioritizes analyzing that question. Also, if a question has low relevance, the analysis unit can postpone analyzing that question. Furthermore, the analysis unit can also adjust the order of analysis according to the relevance of the questions. In this way, by adjusting the order of analysis based on the relevance of the questions, highly relevant questions can be prioritized in analysis.

[0046] When analyzing a question, the analysis unit can adjust the use of terminology according to the user's level of expertise. For example, if the user's level of expertise is high, the analysis unit uses a lot of technical terms. Also, if the user's level of expertise is low, the analysis unit can avoid using technical terms. Furthermore, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. In this way, by adjusting the use of technical terms according to the user's level of expertise, it is possible to provide analysis results that are easy to understand.

[0047] When generating an answer, the generation unit can adjust the accuracy of the answer based on the importance of the question. For example, the generation unit generates a detailed answer for a question with a high level of importance. The generation unit can also generate a simplified answer for a question with a low level of importance. Furthermore, the generation unit can also allocate resources for the answer according to the importance of the question. This allows for efficient allocation of resources by adjusting the level of detail of the answer based on the importance of the question.

[0048] When generating an answer, the generation unit can apply different generation algorithms depending on the category of the question. For example, the generation unit applies a business-oriented generation algorithm to a business-related question. The generation unit can also apply a learning-oriented generation algorithm to a learning-related question. Furthermore, the generation unit can also apply a daily life-oriented generation algorithm to a daily life question. In this way, applying a generation algorithm depending on the question category improves the accuracy of the answer.

[0049] When generating an answer, the generation unit can improve the accuracy of the answer by referring to the user's past answer results. The generation unit improves the accuracy of the answer, for example, based on the user's past answer results. The generation unit can also analyze the user's past answer results and optimize the generation algorithm. Furthermore, the generation unit can adjust the generation parameters by referring to the user's past answer results. In this way, the accuracy of the answer is improved by referring to the user's past answer results.

[0050] When generating answers, the generation unit can determine the order of answers based on the time of submission of the questions. The generation unit can determine the priority of answers based on, for example, the time period in which the questions were submitted. The generation unit can also determine the priority of answers based on the date in which the questions were submitted. Furthermore, the generation unit can also determine the priority of answers based on the timing in which the questions were submitted. This enables timely answers by determining the priority of answers based on the time in which the questions were submitted.

[0051] When generating an answer, the generation unit can adjust the order of answers based on the relevance of the question. For example, if the question has high relevance, the generation unit will preferentially generate an answer to that question. Also, if the question has low relevance, the generation unit can postpone generating an answer to that question. Furthermore, the generation unit can also adjust the order of answers according to the relevance of the question. In this way, by adjusting the order of answers based on the relevance of the question, answers to highly relevant questions can be provided preferentially.

[0052] The generation unit can adjust the use of terminology in response to the user's level of expertise when generating an answer. For example, if the user's level of expertise is high, the generation unit uses a lot of technical terminology. Also, if the user's level of expertise is low, the generation unit can avoid using technical terminology. Furthermore, the generation unit can adjust the use of technical terminology in the answer in response to the user's level of expertise. In this way, by adjusting the use of technical terminology in response to the user's level of expertise, an answer that is easy to understand can be provided.

[0053] When providing an answer, the providing unit can select an appropriate providing method by referring to the user's past feedback. For example, the providing unit preferentially uses a providing method that the user has previously preferred. The providing unit can also suggest an optimal providing method based on the user's past feedback. Furthermore, the providing unit can also customize the providing interface by reflecting the user's past feedback. In this way, the optimal providing method can be selected by referring to the user's past feedback.

[0054] When providing an answer, the providing unit can customize the content to be provided according to the user's current task. For example, if the user is in a business meeting, the providing unit can prioritize providing business-related answers. Also, if the user is studying, the providing unit can prioritize providing study-related answers. Furthermore, if the user is performing daily life tasks, the providing unit can prioritize providing answers related to daily life. In this way, by customizing the content to be provided according to the user's current task, more appropriate answers can be provided.

[0055] When providing an answer, the providing unit can select an appropriate providing method by taking into consideration the user's device information. For example, if the user is using a smartphone, the providing unit can provide a providing method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a providing method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a providing method that is concise and highly visible. This makes it possible to select the optimal providing method by taking into consideration the user's device information.

[0056] When providing an answer, the providing unit can select an appropriate providing method by taking into consideration the user's geographical location information. For example, when the user is in a specific area, the providing unit can preferentially provide answers related to that area. Furthermore, when the user is traveling, the providing unit can preferentially provide answers related to the user's travel destination. Furthermore, when the user is at home, the providing unit can preferentially provide answers related to daily life. In this way, by taking into consideration the user's geographical location information, highly relevant answers can be provided.

[0057] When providing an answer, the providing unit can analyze the user's social media activity and provide a relevant answer. For example, the providing unit can provide an answer related to a topic in which the user has shown interest on social media. The providing unit can also provide a relevant answer based on the content of the user's social media posts. Furthermore, the providing unit can also provide a relevant answer by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to provide a highly relevant answer.

[0058] When providing an answer, the providing unit can provide the content in multiple languages ​​according to the user's language setting. For example, the providing unit automatically sets the language of the answer based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, if the user selects a specific language, the providing unit can also provide the answer in that language. This improves user convenience by providing answers in multiple languages ​​according to the user's language setting.

[0059] When displaying an interface, the UI unit can select an appropriate display method by referring to the user's past operation history. For example, the UI unit preferentially uses a display method that the user has previously preferred. The UI unit can also suggest an optimal display method based on the user's past operation history. Furthermore, the UI unit can also customize the display interface by reflecting the user's past operation history. This allows the optimal display method to be selected by referring to the user's past operation history.

[0060] When displaying the interface, the UI unit can customize the display content according to the user's current task. For example, if the user is in a business meeting, the UI unit can prioritize business-related display content. Also, if the user is studying, the UI unit can prioritize study-related display content. Furthermore, if the user is performing daily life tasks, the UI unit can prioritize daily life-related display content. In this way, by customizing the display content according to the user's current task, more appropriate display content can be provided.

[0061] When displaying an interface, the UI unit can select an appropriate display method by taking into account the user's device information. For example, if the user is using a smartphone, the UI unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the UI unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the UI unit can also provide a simple and highly visible display method. This allows the optimal display method to be selected by taking into account the user's device information.

[0062] When displaying an interface, the UI unit can select an appropriate display method by taking into account the user's device information. For example, if the user is using a smartphone, the UI unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the UI unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the UI unit can also provide a simple and highly visible display method. This allows the optimal display method to be selected by taking into account the user's device information.

[0063] When displaying the interface, the UI unit can make the display content multilingual according to the user's language setting. For example, the UI unit automatically sets the language of the display content based on the language setting of the user's device. The UI unit can also provide a language switching function when the user uses multiple languages. Furthermore, if the user selects a specific language, the UI unit can provide the display content in that language. This improves user convenience by providing multilingual display content according to the user's language setting.

[0064] The UI unit can customize the interface display based on the user's occupation and lifestyle. For example, if the user is a business person, the UI unit can prioritize business-related display content. Also, if the user is a student, the UI unit can prioritize study-related display content. Furthermore, if the user is a housewife, the UI unit can prioritize daily life-related display content. In this way, by customizing the interface based on the user's occupation and lifestyle, more appropriate display content can be provided.

[0065] When responding to a chat, the chat unit can select an appropriate response method by referring to the user's past chat history. For example, the chat unit preferentially uses the response method that the user previously preferred. The chat unit can also suggest the optimal response method based on the user's past chat history. Furthermore, the chat unit can customize the response interface by reflecting the user's past chat history. This allows the optimal response method to be selected by referring to the user's past chat history.

[0066] The chat unit can customize the content of a chat response according to the user's current task. For example, if the user is in a business meeting, the chat unit can prioritize providing business-related responses. If the user is studying, the chat unit can also prioritize providing study-related responses. Furthermore, if the user is performing daily life tasks, the chat unit can also prioritize providing daily life-related responses. This allows the response content to be customized according to the user's current task, making it possible to provide more appropriate responses.

[0067] The chat unit can select an appropriate response method by taking into consideration the user's device information when responding to a chat. For example, if the user is using a smartphone, the chat unit can provide a response method that matches the screen size. Furthermore, if the user is using a tablet, the chat unit can provide a response method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the chat unit can provide a simple and highly visible response method. This allows the optimal response method to be selected by taking into consideration the user's device information.

[0068] The chat unit can select an appropriate response method when responding to a chat by taking into consideration the user's geographical location information. For example, if the user is in a specific area, the chat unit can prioritize providing responses related to that area. Also, if the user is traveling, the chat unit can prioritize providing responses related to the user's travel destination. Furthermore, if the user is at home, the chat unit can prioritize providing responses related to daily life. In this way, highly relevant responses can be provided by taking into consideration the user's geographical location information.

[0069] The chat unit can analyze the user's social media activity and provide a relevant response when chatting. For example, the chat unit can provide a response related to a topic in which the user has shown interest on social media. The chat unit can also provide a relevant response based on the content of the user's social media posts. Furthermore, the chat unit can also provide a relevant response by referring to the activity of the user's friends on social media. In this way, highly relevant responses can be provided by analyzing the user's social media activity.

[0070] The chat unit can respond to chats in multiple languages ​​according to the user's language setting. For example, the chat unit automatically sets the language of the response based on the language setting of the user's device. The chat unit can also provide a language switching function when the user uses multiple languages. Furthermore, if the user selects a specific language, the chat unit can provide responses in that language. This improves user convenience by providing responses in multiple languages ​​according to the user's language setting.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The reception unit can analyze the user's past question history and suggest new related questions based on the types of questions the user has asked in the past. For example, if the user has asked many questions about business strategies in the past, the reception unit can suggest questions about new business strategies. Also, if the user has asked many questions about learning methods in the past, the reception unit can suggest questions about new learning methods. Furthermore, if the user has shown interest in a particular topic in the past, the reception unit can suggest new questions related to that topic. In this way, the user's past question history can be utilized to suggest more relevant questions.

[0073] The analysis unit can evaluate the reliability of answers to user questions and provide highly reliable answers preferentially. For example, the analysis unit can refer to a highly reliable information source from a past database to evaluate the reliability of the answer. The analysis unit can also evaluate the reliability of the answer based on user feedback. Furthermore, the analysis unit can integrate data from multiple information sources to generate highly reliable answers. This makes it possible to provide highly reliable answers to users.

[0074] The generator can take into account the diversity of answers when generating answers to a user's questions. For example, the generator can generate answers from multiple different perspectives for the same question. The generator can also generate multiple different answers based on data from different information sources. Furthermore, the generator can change the style or format of the answer depending on the user's preferences. This allows the user to be provided with answers from multiple perspectives.

[0075] The providing unit can adjust the method of providing the answer depending on the usage status of the user's device. For example, if the user is using a smartphone, a short and concise answer can be provided. If the user is using a personal computer, a detailed answer can be provided. Furthermore, if the user is using a tablet, a visually rich answer can be provided. This makes it possible to provide the optimal answer depending on the usage status of the user's device.

[0076] The UI unit can customize the interface based on the user's operation history. For example, it can prioritize providing the operation methods that the user has previously preferred. It can also prioritize displaying functions that the user has previously used. It can also analyze the user's operation history and suggest the optimal interface. This makes it possible to utilize the user's operation history to provide an easier-to-use interface.

[0077] The processing flow of the first embodiment will be briefly explained below.

[0078] Step 1: The reception unit receives a question from a user. For example, the user can input the question through video chat. Step 2: The analysis unit uses the generation AI to analyze the question received by the reception unit. For example, the generation AI uses natural language processing technology to understand and analyze the content of the question. Step 3: The generator uses the generation AI to generate an answer based on the question analyzed by the analysis unit. For example, the generation AI may refer to past data and case studies to generate an answer. Step 4: The providing unit provides the answer generated by the generating unit to the user in real time, for example, through a video chat.

[0079] (Example 2) A system according to an embodiment of the present invention utilizes the wisdom of historical figures to solve various problems and questions faced by users. In this system, users input questions via video chat, and a generative AI analyzes the questions and generates answers based on the wisdom of historical figures. The system then delivers the answers to users in real time. For example, if a user inputs a question such as, "I'm thinking about a new business strategy. How should I proceed?", the generative AI generates answers based on the strategic thinking of Napoleon Bonaparte and the innovative ideas of Steve Jobs and delivers them in real time. This system allows users to receive fast and accurate advice. For example, if a student asks, "What is the most efficient way to study for exams?", the system provides advice based on Einstein's study methods and Benjamin Franklin's time management techniques. This system helps users solve problems and offers new perspectives and concrete solutions. For example, this system can provide fast and accurate advice not only to business leaders, but also to students, teachers, and anyone faced with daily decision-making.

[0080] A problem solving support system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a provision unit. The reception unit receives questions from users. For example, users can input questions through video chat. The analysis unit uses a generation AI to analyze the questions received by the reception unit. For example, the generation AI understands and analyzes the content of the questions using natural language processing technology. The generation unit uses the generation AI to generate answers based on the questions analyzed by the analysis unit. For example, the generation AI generates answers by referring to past data and case studies. The provision unit provides the answers generated by the generation unit to users in real time. For example, the answers are provided through video chat. This enables the problem solving support system according to an embodiment to efficiently analyze users' questions and provide appropriate answers.

[0081] The analysis unit can analyze the question using natural language processing technology. Natural language processing technology includes, for example, morphological analysis, grammatical analysis, and semantic analysis. The analysis unit, for example, uses morphological analysis to divide the words in the question and analyze the meaning of each word. The analysis unit can also use grammatical analysis to analyze the sentence structure of the question and understand the meaning of the sentence. Furthermore, the analysis unit can use semantic analysis to understand the intent of the question and extract information for generating an appropriate answer. As a result, the use of natural language processing technology improves the accuracy of question analysis.

[0082] The generation unit can generate an answer by referring to past data or examples. Past data and examples include, for example, a past FAQ database and a user's question history. The generation unit can, for example, refer to a past FAQ database to generate an answer to a similar question. The generation unit can also refer to the user's question history to generate a new answer based on answers provided to similar questions in the past. Furthermore, the generation unit can also refer to past examples to generate an answer based on a specific example. In this way, by referring to past data and examples, the reliability of the answer is improved.

[0083] The providing unit can provide answers in real time through video chat. Specific video chat technologies include, for example, a video chat platform to be used, a communication protocol, and the like. The providing unit provides answers to users using, for example, a specific video chat platform. The providing unit can also use a specific communication protocol to achieve real-time communication. Furthermore, the providing unit can adjust the image quality and sound quality of the video chat to provide high-quality answers to users. This improves user convenience by providing answers in real time through video chat.

[0084] The problem-solving support system includes a UI unit that manages the user interface. The UI unit manages the user interface. For example, the UI unit designs the screen layout to allow users to operate it intuitively. The UI unit can also define operation methods to allow users to easily input questions. Furthermore, the UI unit can manage the displayed content and provide the user with the information they need appropriately. In this way, by managing the user interface, user operability is improved.

[0085] The problem solving support system includes a chat unit that provides a video chat function. The chat unit provides the video chat function. For example, the chat unit manages the image quality and sound quality of the video chat to provide the user with a high-quality video chat experience. The chat unit can also use a specific communication protocol to achieve real-time communication. Furthermore, the chat unit can monitor the connection status of the video chat to prevent the connection from being interrupted. As a result, the video chat function is provided, allowing the user to ask questions in real time.

[0086] The reception unit can estimate the user's emotions and adjust the timing of question reception based on the emotions. For example, if the user is feeling stressed, the reception unit can receive questions at a timing when the user can relax. Furthermore, if the user is concentrating, the reception unit can also receive questions immediately. Furthermore, if the user is tired, the reception unit can also receive questions after a break. In this way, by adjusting the timing of question reception according to the user's emotions, questions can be received at a more appropriate timing. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0087] The reception unit can analyze the user's past question history and select the optimal reception method. For example, the reception unit can preferentially receive question formats that the user has frequently used in the past. The reception unit can also preferentially receive questions related to topics that the user has previously liked. Furthermore, the reception unit can also suggest the optimal reception time based on the user's past question history. In this way, the optimal reception method can be selected by analyzing the user's past question history.

[0088] When receiving a question, the reception unit can filter the questions based on the user's current areas of interest. For example, the reception unit can only accept questions related to topics in which the user is currently interested. The reception unit can also filter related questions based on the user's recent search history. Furthermore, the reception unit can analyze the user's social media activity and filter questions based on the user's areas of interest. In this way, by filtering questions based on the user's areas of interest, highly relevant questions can be accepted.

[0089] When accepting a question, the acceptance unit can select an appropriate acceptance means depending on the user's input method. For example, if the user prefers voice input, the acceptance unit can preferentially accept voice input. Also, if the user prefers text input, the acceptance unit can preferentially accept text input. Furthermore, if the user uses an image to ask a question, the acceptance unit can perform image analysis and accept the question. This improves user convenience by selecting the optimal acceptance means depending on the user's input method.

[0090] The reception unit can estimate the user's emotions and determine the priority of questions to be received based on the emotions. For example, if the user feels urgent, the reception unit can receive that question with the highest priority. Furthermore, if the user is relaxed, the reception unit can also receive questions with normal priority. Furthermore, if the user feels anxious, the reception unit can also receive those questions earlier. In this way, by determining the priority of questions according to the user's emotions, important questions can be received with priority. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0091] When accepting questions, the acceptance unit can prioritize accepting relevant questions by taking into account the user's geographical location information. For example, if the user is in a specific area, the acceptance unit can prioritize accepting questions related to that area. Furthermore, if the user is traveling, the acceptance unit can also prioritize accepting questions related to the user's travel destination. Furthermore, if the user is at home, the acceptance unit can also prioritize accepting questions related to daily life. In this way, by taking into account the user's geographical location information, highly relevant questions can be prioritized.

[0092] When receiving a question, the reception unit can analyze the user's social media activity and receive related questions. For example, the reception unit receives questions related to topics in which the user has shown interest on social media. The reception unit can also receive related questions based on the content of the user's social media posts. Furthermore, the reception unit can also receive related questions by referring to the activities of the user's friends on social media. In this way, highly relevant questions can be received by analyzing the user's social media activity.

[0093] The reception unit can adjust the reception method by reflecting the user's past feedback when receiving a question. For example, the reception unit preferentially uses the reception method that the user has previously preferred. The reception unit can also suggest the optimal reception method based on the user's past feedback. Furthermore, the reception unit can also customize the reception interface by reflecting the user's past feedback. In this way, the optimal reception method can be provided by reflecting the user's past feedback.

[0094] The analysis unit can estimate the user's emotions and adjust the question analysis method based on the emotions. For example, if the user is nervous, the analysis unit can use a simple analysis method. If the user is relaxed, the analysis unit can also use a detailed analysis method. Furthermore, if the user is in a hurry, the analysis unit can also use a quick analysis method. This allows for more appropriate analysis by adjusting the question analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0095] When analyzing a question, the analysis unit can adjust the accuracy of the analysis based on the importance of the question. For example, the analysis unit performs a detailed analysis on a question with a high level of importance. The analysis unit can also perform a simplified analysis on a question with a low level of importance. Furthermore, the analysis unit can allocate analysis resources according to the importance of the question. This allows for efficient allocation of resources by adjusting the level of detail of the analysis based on the importance of the question.

[0096] When analyzing a question, the analysis unit can apply different analysis algorithms depending on the category of the question. For example, the analysis unit applies a business-oriented analysis algorithm to a business-related question. The analysis unit can also apply a learning-oriented analysis algorithm to a learning-related question. Furthermore, the analysis unit can also apply an everyday life-oriented analysis algorithm to a question about everyday life. In this way, by applying an analysis algorithm depending on the question category, the accuracy of the analysis is improved.

[0097] When analyzing a question, the analysis unit can improve the accuracy of the analysis by referring to the user's past question results. The analysis unit improves the accuracy of the analysis, for example, based on the user's past question results. The analysis unit can also analyze the user's past question results and optimize the analysis algorithm. Furthermore, the analysis unit can adjust the analysis parameters by referring to the user's past question results. In this way, the accuracy of the analysis is improved by referring to the user's past question results.

[0098] The analysis unit can estimate the user's emotions and determine the analysis priority based on the emotions. For example, if the user feels urgent, the analysis unit gives the highest priority to analyzing that question. Also, if the user is relaxed, the analysis unit can analyze the question with normal priority. Furthermore, if the user feels anxious, the analysis unit can analyze the question earlier. In this way, by determining the analysis priority according to the user's emotions, important questions can be analyzed preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0099] When analyzing questions, the analysis unit can determine the order of analysis based on the time of submission of the questions. The analysis unit can determine the priority of analysis based on, for example, the time period in which the questions were submitted. The analysis unit can also determine the priority of analysis based on the date in which the questions were submitted. Furthermore, the analysis unit can also determine the priority of analysis based on the timing in which the questions were submitted. In this way, by determining the priority of analysis based on the time in which the questions were submitted, timely analysis is possible.

[0100] When analyzing questions, the analysis unit can adjust the order of analysis based on the relevance of the questions. For example, if a question has high relevance, the analysis unit prioritizes analyzing that question. Also, if a question has low relevance, the analysis unit can postpone analyzing that question. Furthermore, the analysis unit can also adjust the order of analysis according to the relevance of the questions. In this way, by adjusting the order of analysis based on the relevance of the questions, highly relevant questions can be prioritized in analysis.

[0101] When analyzing a question, the analysis unit can adjust the use of terminology according to the user's level of expertise. For example, if the user's level of expertise is high, the analysis unit uses a lot of technical terms. Also, if the user's level of expertise is low, the analysis unit can avoid using technical terms. Furthermore, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. In this way, by adjusting the use of technical terms according to the user's level of expertise, it is possible to provide analysis results that are easy to understand.

[0102] The generation unit can estimate the user's emotions and adjust the answer generation method based on the emotions. For example, if the user is relaxed, the generation unit generates a detailed answer. Also, if the user is in a hurry, the generation unit can generate a concise answer. Furthermore, if the user is excited, the generation unit can generate a visually stimulating answer. This allows for adjusting the answer generation method according to the user's emotions, thereby providing a more appropriate answer. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0103] When generating an answer, the generation unit can adjust the accuracy of the answer based on the importance of the question. For example, the generation unit generates a detailed answer for a question with a high level of importance. The generation unit can also generate a simplified answer for a question with a low level of importance. Furthermore, the generation unit can also allocate resources for the answer according to the importance of the question. This allows for efficient allocation of resources by adjusting the level of detail of the answer based on the importance of the question.

[0104] When generating an answer, the generation unit can apply different generation algorithms depending on the category of the question. For example, the generation unit applies a business-oriented generation algorithm to a business-related question. The generation unit can also apply a learning-oriented generation algorithm to a learning-related question. Furthermore, the generation unit can also apply a daily life-oriented generation algorithm to a daily life question. In this way, applying a generation algorithm depending on the question category improves the accuracy of the answer.

[0105] When generating an answer, the generation unit can improve the accuracy of the answer by referring to the user's past answer results. The generation unit improves the accuracy of the answer, for example, based on the user's past answer results. The generation unit can also analyze the user's past answer results and optimize the generation algorithm. Furthermore, the generation unit can adjust the generation parameters by referring to the user's past answer results. In this way, the accuracy of the answer is improved by referring to the user's past answer results.

[0106] The generation unit can estimate the user's emotions and adjust the length of the answer based on the emotions. For example, if the user is in a hurry, the generation unit can generate a short, to-the-point answer. If the user is relaxed, the generation unit can also generate a longer answer with detailed explanations. Furthermore, if the user is excited, the generation unit can also generate an answer with a visually stimulating effect. This allows for adjusting the length of the answer according to the user's emotions, thereby providing a more appropriate answer. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0107] When generating answers, the generation unit can determine the order of answers based on the time of submission of the questions. The generation unit can determine the priority of answers based on, for example, the time period in which the questions were submitted. The generation unit can also determine the priority of answers based on the date in which the questions were submitted. Furthermore, the generation unit can also determine the priority of answers based on the timing in which the questions were submitted. This enables timely answers by determining the priority of answers based on the time in which the questions were submitted.

[0108] When generating an answer, the generation unit can adjust the order of answers based on the relevance of the question. For example, if the question has high relevance, the generation unit will preferentially generate an answer to that question. Also, if the question has low relevance, the generation unit can postpone generating an answer to that question. Furthermore, the generation unit can also adjust the order of answers according to the relevance of the question. In this way, by adjusting the order of answers based on the relevance of the question, answers to highly relevant questions can be provided preferentially.

[0109] The generation unit can adjust the use of terminology in response to the user's level of expertise when generating an answer. For example, if the user's level of expertise is high, the generation unit uses a lot of technical terminology. Also, if the user's level of expertise is low, the generation unit can avoid using technical terminology. Furthermore, the generation unit can adjust the use of technical terminology in the answer in response to the user's level of expertise. In this way, by adjusting the use of technical terminology in response to the user's level of expertise, an answer that is easy to understand can be provided.

[0110] The providing unit can estimate the user's emotions and adjust the way in which the answer is provided based on the emotions. For example, if the user is nervous, the providing unit can provide the answer in a calm voice. Furthermore, if the user is relaxed, the providing unit can also provide the answer in a cheerful voice. Furthermore, if the user is in a hurry, the providing unit can also provide a quick and concise answer. In this way, by adjusting the way in which the answer is provided according to the user's emotions, the answer can be provided in a more appropriate manner. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0111] When providing an answer, the providing unit can select an appropriate providing method by referring to the user's past feedback. For example, the providing unit preferentially uses a providing method that the user has previously preferred. The providing unit can also suggest an optimal providing method based on the user's past feedback. Furthermore, the providing unit can also customize the providing interface by reflecting the user's past feedback. In this way, the optimal providing method can be selected by referring to the user's past feedback.

[0112] When providing an answer, the providing unit can customize the content to be provided according to the user's current task. For example, if the user is in a business meeting, the providing unit can prioritize providing business-related answers. Also, if the user is studying, the providing unit can prioritize providing study-related answers. Furthermore, if the user is performing daily life tasks, the providing unit can prioritize providing answers related to daily life. In this way, by customizing the content to be provided according to the user's current task, more appropriate answers can be provided.

[0113] When providing an answer, the providing unit can select an appropriate providing method by taking into consideration the user's device information. For example, if the user is using a smartphone, the providing unit can provide a providing method that matches the screen size. Furthermore, if the user is using a tablet, the providing unit can also provide a providing method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the providing unit can also provide a providing method that is concise and highly visible. This makes it possible to select the optimal providing method by taking into consideration the user's device information.

[0114] The providing unit can estimate the user's emotions and determine the priority of answers to be provided based on the emotions. For example, if the user feels urgent, the providing unit provides that answer with the highest priority. Furthermore, if the user is relaxed, the providing unit can also provide an answer with normal priority. Furthermore, if the user feels anxious, the providing unit can also provide that answer earlier. In this way, by determining the priority of answers according to the user's emotions, important answers can be provided preferentially. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0115] When providing an answer, the providing unit can select an appropriate providing method by taking into consideration the user's geographical location information. For example, when the user is in a specific area, the providing unit can preferentially provide answers related to that area. Furthermore, when the user is traveling, the providing unit can preferentially provide answers related to the user's travel destination. Furthermore, when the user is at home, the providing unit can preferentially provide answers related to daily life. In this way, by taking into consideration the user's geographical location information, highly relevant answers can be provided.

[0116] When providing an answer, the providing unit can analyze the user's social media activity and provide a relevant answer. For example, the providing unit can provide an answer related to a topic in which the user has shown interest on social media. The providing unit can also provide a relevant answer based on the content of the user's social media posts. Furthermore, the providing unit can also provide a relevant answer by referring to the activity of the user's friends on social media. In this way, by analyzing the user's social media activity, it is possible to provide a highly relevant answer.

[0117] When providing an answer, the providing unit can provide the content in multiple languages ​​according to the user's language setting. For example, the providing unit automatically sets the language of the answer based on the language setting of the user's device. The providing unit can also provide a language switching function when the user uses multiple languages. Furthermore, if the user selects a specific language, the providing unit can also provide the answer in that language. This improves user convenience by providing answers in multiple languages ​​according to the user's language setting.

[0118] The UI unit can estimate the user's emotions and adjust the interface display method based on the emotions. For example, if the user is nervous, the UI unit can provide an interface with subdued colors to reduce visual stress. Furthermore, if the user is having fun, the UI unit can provide an interface with bright colors to make input work more enjoyable. Furthermore, if the user is tired, the UI unit can provide a simple, highly visible interface to make input work easier. This allows visual stress to be reduced by adjusting the interface display method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0119] When displaying an interface, the UI unit can select an appropriate display method by referring to the user's past operation history. For example, the UI unit preferentially uses a display method that the user has previously preferred. The UI unit can also suggest an optimal display method based on the user's past operation history. Furthermore, the UI unit can also customize the display interface by reflecting the user's past operation history. This allows the optimal display method to be selected by referring to the user's past operation history.

[0120] When displaying the interface, the UI unit can customize the display content according to the user's current task. For example, if the user is in a business meeting, the UI unit can prioritize business-related display content. Also, if the user is studying, the UI unit can prioritize study-related display content. Furthermore, if the user is performing daily life tasks, the UI unit can prioritize daily life-related display content. In this way, by customizing the display content according to the user's current task, more appropriate display content can be provided.

[0121] When displaying an interface, the UI unit can select an appropriate display method by taking into account the user's device information. For example, if the user is using a smartphone, the UI unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the UI unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the UI unit can also provide a simple and highly visible display method. This allows the optimal display method to be selected by taking into account the user's device information.

[0122] The UI unit can estimate the user's emotions and adjust the interface operation procedures based on the emotions. For example, if the user is nervous, the UI unit can provide simple and intuitive operation procedures. Furthermore, if the user is relaxed, the UI unit can provide detailed operation procedures. Furthermore, if the user is in a hurry, the UI unit can provide procedures that allow quick operation. This allows the operation procedures to be adjusted according to the user's emotions, thereby providing a more intuitive and easy-to-use interface. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0123] When displaying an interface, the UI unit can select an appropriate display method by taking into account the user's device information. For example, if the user is using a smartphone, the UI unit provides a display method that matches the screen size. Furthermore, if the user is using a tablet, the UI unit can also provide a display method optimized for a large screen. Furthermore, if the user is using a smartwatch, the UI unit can also provide a simple and highly visible display method. This allows the optimal display method to be selected by taking into account the user's device information.

[0124] When displaying the interface, the UI unit can make the display content multilingual according to the user's language setting. For example, the UI unit automatically sets the language of the display content based on the language setting of the user's device. The UI unit can also provide a language switching function when the user uses multiple languages. Furthermore, if the user selects a specific language, the UI unit can provide the display content in that language. This improves user convenience by providing multilingual display content according to the user's language setting.

[0125] The UI unit can customize the interface display based on the user's occupation and lifestyle. For example, if the user is a business person, the UI unit can prioritize business-related display content. Also, if the user is a student, the UI unit can prioritize study-related display content. Furthermore, if the user is a housewife, the UI unit can prioritize daily life-related display content. In this way, by customizing the interface based on the user's occupation and lifestyle, more appropriate display content can be provided.

[0126] The chat unit can estimate the user's emotions and adjust the chat response method based on the emotions. For example, if the user is nervous, the chat unit can respond in a calm voice. Also, if the user is relaxed, the chat unit can respond in a cheerful voice. Furthermore, if the user is in a hurry, the chat unit can provide a quick and concise response. This allows the chat response method to be adjusted according to the user's emotions, thereby providing a more appropriate response. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0127] When responding to a chat, the chat unit can select an appropriate response method by referring to the user's past chat history. For example, the chat unit preferentially uses the response method that the user previously preferred. The chat unit can also suggest the optimal response method based on the user's past chat history. Furthermore, the chat unit can customize the response interface by reflecting the user's past chat history. This allows the optimal response method to be selected by referring to the user's past chat history.

[0128] The chat unit can customize the content of a chat response according to the user's current task. For example, if the user is in a business meeting, the chat unit can prioritize providing business-related responses. If the user is studying, the chat unit can also prioritize providing study-related responses. Furthermore, if the user is performing daily life tasks, the chat unit can also prioritize providing daily life-related responses. This allows the response content to be customized according to the user's current task, making it possible to provide more appropriate responses.

[0129] The chat unit can select an appropriate response method by taking into consideration the user's device information when responding to a chat. For example, if the user is using a smartphone, the chat unit can provide a response method that matches the screen size. Furthermore, if the user is using a tablet, the chat unit can provide a response method that is optimized for a large screen. Furthermore, if the user is using a smartwatch, the chat unit can provide a simple and highly visible response method. This allows the optimal response method to be selected by taking into consideration the user's device information.

[0130] The chat unit can estimate the user's emotions and determine the priority of chats based on the emotions. For example, if the user feels urgent, the chat unit responds to that chat with the highest priority. Also, if the user feels relaxed, the chat unit can respond with normal priority. Furthermore, if the user feels anxious, the chat unit can respond to that chat earlier. In this way, by determining the priority of chats according to the user's emotions, important chats can be responded to preferentially. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples.

[0131] The chat unit can select an appropriate response method when responding to a chat by taking into consideration the user's geographical location information. For example, if the user is in a specific area, the chat unit can prioritize providing responses related to that area. Also, if the user is traveling, the chat unit can prioritize providing responses related to the user's travel destination. Furthermore, if the user is at home, the chat unit can prioritize providing responses related to daily life. In this way, highly relevant responses can be provided by taking into consideration the user's geographical location information.

[0132] The chat unit can analyze the user's social media activity and provide a relevant response when chatting. For example, the chat unit can provide a response related to a topic in which the user has shown interest on social media. The chat unit can also provide a relevant response based on the content of the user's social media posts. Furthermore, the chat unit can also provide a relevant response by referring to the activity of the user's friends on social media. In this way, highly relevant responses can be provided by analyzing the user's social media activity.

[0133] The chat unit can respond to chats in multiple languages ​​according to the user's language setting. For example, the chat unit automatically sets the language of the response based on the language setting of the user's device. The chat unit can also provide a language switching function when the user uses multiple languages. Furthermore, if the user selects a specific language, the chat unit can provide responses in that language. This improves user convenience by providing responses in multiple languages ​​according to the user's language setting. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, provision unit, UI unit, and chat unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the reception device 38 of the smart device 14 and receives questions from users. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the questions using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates answers based on the analyzed questions. The provision unit is realized by the output device 40 of the smart device 14 and provides the generated answers to the user in real time. The UI unit is realized by the control unit 46A of the smart device 14 and manages the user interface. The chat unit is realized by the communication I / F 44 of the smart device 14 and provides video chat functionality. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, provision unit, UI unit, and chat unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the smart glasses 214 and receives a question from a user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the question using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an answer based on the analyzed question. The provision unit is realized by the speaker 240 of the smart glasses 214 and provides the generated answer to the user in real time. The UI unit is realized by the control unit 46A of the smart glasses 214 and manages the user interface. The chat unit is realized by the communication I / F 44 of the smart glasses 214 and provides a video chat function. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, provision unit, UI unit, and chat unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the headset type terminal 314 and receives questions from a user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the questions using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates answers based on the analyzed questions. The provision unit is realized by the speaker 240 of the headset type terminal 314 and provides the generated answers to the user in real time. The UI unit is realized by the control unit 46A of the headset type terminal 314 and manages the user interface. The chat unit is realized by the communication I / F 44 of the headset type terminal 314 and provides a video chat function. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, generation unit, provision unit, UI unit, and chat unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the microphone 238 of the robot 414 and receives questions from a user. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the question using a generation AI. The generation unit is realized by the specific processing unit 290 of the data processing device 12 and generates an answer based on the analyzed question. The provision unit is realized by the speaker 240 of the robot 414 and provides the generated answer to the user in real time. The UI unit is realized by the control unit 46A of the robot 414 and manages the user interface. The chat unit is realized by the communication I / F 44 of the robot 414 and provides a video chat function.

[0134] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0135] The reception unit can analyze the user's past question history and suggest new related questions based on the types of questions the user has asked in the past. For example, if the user has asked many questions about business strategies in the past, the reception unit can suggest questions about new business strategies. Also, if the user has asked many questions about learning methods in the past, the reception unit can suggest questions about new learning methods. Furthermore, if the user has shown interest in a particular topic in the past, the reception unit can suggest new questions related to that topic. In this way, the user's past question history can be utilized to suggest more relevant questions.

[0136] The analysis unit can evaluate the reliability of answers to user questions and provide highly reliable answers preferentially. For example, the analysis unit can refer to a highly reliable information source from a past database to evaluate the reliability of the answer. The analysis unit can also evaluate the reliability of the answer based on user feedback. Furthermore, the analysis unit can integrate data from multiple information sources to generate highly reliable answers. This makes it possible to provide highly reliable answers to users.

[0137] The generator can take into account the diversity of answers when generating answers to a user's questions. For example, the generator can generate answers from multiple different perspectives for the same question. The generator can also generate multiple different answers based on data from different information sources. Furthermore, the generator can change the style or format of the answer depending on the user's preferences. This allows the user to be provided with answers from multiple perspectives.

[0138] The providing unit can adjust the method of providing the answer depending on the usage status of the user's device. For example, if the user is using a smartphone, a short and concise answer can be provided. If the user is using a personal computer, a detailed answer can be provided. Furthermore, if the user is using a tablet, a visually rich answer can be provided. This makes it possible to provide the optimal answer depending on the usage status of the user's device.

[0139] The UI unit can customize the interface based on the user's operation history. For example, it can prioritize providing the operation methods that the user has previously preferred. It can also prioritize displaying functions that the user has previously used. It can also analyze the user's operation history and suggest the optimal interface. This makes it possible to utilize the user's operation history to provide an easier-to-use interface.

[0140] The reception unit can estimate the user's emotions and adjust the way questions are received based on the emotions. For example, if the user is feeling stressed, a relaxing interface can be provided. If the user is concentrating, a simple and intuitive interface can be provided. Furthermore, if the user is tired, a visually calming interface can be provided. In this way, it is possible to provide an optimal interface according to the user's emotions.

[0141] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the emotions. For example, if the user is nervous, a simple analysis method can be used. If the user is relaxed, a detailed analysis method can be used. Furthermore, if the user is in a hurry, a quick analysis method can be used. In this way, by adjusting the accuracy of the analysis according to the user's emotions, more appropriate analysis can be performed.

[0142] The generation unit can estimate the user's emotions and adjust the style of the answer based on the emotions. For example, if the user is relaxed, a detailed and polite answer can be generated. If the user is in a hurry, a concise and to-the-point answer can be generated. Furthermore, if the user is excited, a visually stimulating answer can be generated. In this way, by adjusting the style of the answer according to the user's emotions, more appropriate answers can be provided.

[0143] The providing unit can estimate the user's emotions and adjust the way in which the answer is provided based on the emotions. For example, if the user is nervous, the answer can be provided in a calm voice. If the user is relaxed, the answer can be provided in a cheerful voice. Furthermore, if the user is in a hurry, the answer can be provided quickly and concisely. In this way, by adjusting the way in which the answer is provided according to the user's emotions, the answer can be provided in a more appropriate manner.

[0144] The UI unit can estimate the user's emotions and adjust the interface display method based on the emotions. For example, if the user is nervous, it can provide an interface with calm colors to reduce visual stress. If the user is having fun, it can provide an interface with bright colors to make input work more enjoyable. Furthermore, if the user is tired, it can provide a simple, highly visible interface to make input work easier. In this way, visual stress can be reduced by adjusting the interface display method according to the user's emotions.

[0145] The processing flow of the second embodiment will be briefly explained below.

[0146] Step 1: The reception unit receives a question from a user. For example, the user can input the question through video chat. Step 2: The analysis unit uses the generation AI to analyze the question received by the reception unit. For example, the generation AI uses natural language processing technology to understand and analyze the content of the question. Step 3: The generator uses the generation AI to generate an answer based on the question analyzed by the analysis unit. For example, the generation AI may refer to past data and case studies to generate an answer. Step 4: The providing unit provides the answer generated by the generating unit to the user in real time, for example, through a video chat.

[0147] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0148] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0149] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0150] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0151] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0152] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0153] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0154] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0155] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0156] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0157] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0158] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0159] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0160] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0161] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0162] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0163] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0164] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0165] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0166] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0167] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0168] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0169] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0170] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0171] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0172] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0173] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0174] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0175] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0176] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0177] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0178] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0179] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0180] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0181] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0182] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0183] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0184] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0185] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0186] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0187] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0188] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0189] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0190] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0191] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0192] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0193] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0194] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0195] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0196] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0197] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0198] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0199] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0200] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0201] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0202] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0203] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0204] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0205] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0206] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0207] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0208] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0209] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0210] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0211] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0212] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0213] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0214] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0215] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0216] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0217] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0218] [Explanation of symbols]

[0219] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives questions from users; an analysis unit that analyzes the question received by the reception unit; a generation unit that generates an answer based on the question analyzed by the analysis unit; a providing unit that provides the answer generated by the generating unit; Equipped with A system characterized by:

2. The analysis unit Analyzing questions using natural language processing technology 2. The system of claim 1.

3. The generation unit Reference historical data or examples to generate answers 2. The system of claim 1.

4. The providing unit Provide answers in real time via video chat 2. The system of claim 1.

5. It has a UI section that manages the user interface.

2. The system of claim 1.

6. It has a chat section that provides video chat functionality 2. The system of claim 1.

7. The reception unit Estimates the user's emotions and adjusts the timing of accepting questions based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past question history and select the optimal reception method 2. The system of claim 1.

9. The reception unit When questions are asked, they are filtered based on the user's current interests.

2. The system of claim 1.

10. The reception unit When accepting a question, select the appropriate acceptance method depending on the user's input method.

2. The system of claim 1.

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